The SHOGUN machine learning toolbox We have developed a machine learning toolbox, called SHOGUN, which is designed for unified large-scale learning for a broad range of feature types and learning settings. It offers a considerable number of machine learning models such as support vector machines, hidden Markov models, multiple kernel learning, linear discriminant analysis, and more. Most of the specific algorithms are able to deal with several different data classes. We have used this toolbox in several applications from computational biology, some of them coming with no less than 50 million training examples and others with 7 billion test examples. With more than a thousand installations worldwide, SHOGUN is already widely adopted in the machine learning community and beyond. SHOGUN is implemented in C++ and interfaces to MATLAB TM , R, Octave, Python, and has a stand-alone command line interface. The source code is freely available under the GNU General Public License, Version 3 at

References in zbMATH (referenced in 65 articles , 2 standard articles )

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  1. Andrea Esuli, Tiziano Fagni, Alejandro Moreo Fernandez: JaTeCS an open-source JAva TExt Categorization System (2017) arXiv
  2. Lema^ıtre, Guillaume; Nogueira, Fernando; Aridas, Christos K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning (2017)
  3. Liu, Weiwei; Tsang, Ivor W.: Making decision trees feasible in ultrahigh feature and label dimensions (2017)
  4. Ryan R. Curtin, Shikhar Bhardwaj, Marcus Edel, Yannis Mentekidis: A generic and fast C++ optimization framework (2017) arXiv
  5. Antoniuk, Kostiantyn; Franc, Vojtěch; Hlaváč, Václav: V-shaped interval insensitive loss for ordinal classification (2016)
  6. Christmann, Andreas; Dumpert, Florian; Xiang, Dao-Hong: On extension theorems and their connection to universal consistency in machine learning (2016)
  7. Gondzio, Jacek; González-Brevis, Pablo; Munari, Pedro: Large-scale optimization with the primal-dual column generation method (2016)
  8. Qi, Chengming; Wang, Yuping; Tian, Wenjie; Wang, Qun: Multiple kernel boosting framework based on information measure for classification (2016)
  9. Rieck, Konrad; Wressnegger, Christian: Harry: a tool for measuring string similarity (2016)
  10. Huang, Shuangping; Jin, Lianwen; Xue, Kunnan; Fang, Yuan: Online primal-dual learning for a data-dependent multi-kernel combination model with multiclass visual categorization applications (2015)
  11. Wang, Zhe; Fan, Qi; Ke, Sheng; Gao, Daqi: Structural multiple empirical kernel learning (2015)
  12. Carrizosa, Emilio; Martín-Barragán, Belén; Morales, Dolores Romero: A nested heuristic for parameter tuning in support vector machines (2014)
  13. Kartelj, Aleksandar; Mitić, Nenad; Filipović, Vladimir; Tošić, Dušan: Electromagnetism-like algorithm for support vector machine parameter tuning (2014) ioport
  14. Krawczyk, Bartosz; Woźniak, Michał; Cyganek, Bogusław: Clustering-based ensembles for one-class classification (2014)
  15. Pan, Binbin; Lai, Jianhuang; Shen, Lixin: Ideal regularization for learning kernels from labels (2014)
  16. Sun, Zhonggui; Chen, Songcan; Qiao, Lishan: A general non-local denoising model using multi-kernel-induced measures (2014)
  17. Afkanpour, Arash; Szepesvári, Csaba; Bowling, Michael: Alignment based kernel learning with a continuous set of base kernels (2013)
  18. Curtin, Ryan R.; Cline, James R.; Slagle, N.P.; March, William B.; Ram, Parikshit; Mehta, Nishant A.; Gray, Alexander G.: MLPACK: a scalable C++ machine learning library (2013)
  19. Hoi, Steven C.H.; Jin, Rong; Zhao, Peilin; Yang, Tianbao: Online multiple kernel classification (2013)
  20. Liang, Zhizheng; Xia, Shixiong; Zhou, Yong; Zhang, Lei: Training Lp norm multiple kernel learning in the primal (2013)

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